from tensorflow.examples.tutorials.mnist import input_data
import  tensorflow as tf

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder("float", [None, 784])
# y_ 是准确的值
y_ = tf.placeholder("float", [None, 10])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# y 是预测值
y = tf.nn.softmax(tf.matmul(x,W) + b)

# cross_entropy 是 交叉熵，是常见的成本函数(用来评价模型的好坏)
# 这里交叉熵用的是100个图片的交叉熵综合，做判断
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    # 取下100个数据集作为训练计算的数据 以及 实际的值 放到占位符 里面去
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    # 可以用feed_dict来替代任何张量，并不仅限于替换占位符。
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))



